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Energy Losses in Transformers01:21

Energy Losses in Transformers

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In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
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Instrument transformers, comprising voltage transformers (VTs) and current transformers (CTs), play crucial roles in power substations by providing isolated replicas of current or voltage for measurement and protection purposes. Voltage transformers reduce the primary voltage to levels suitable for relay operation and measurement, while current transformers scale down the primary current. The primary winding of a current transformer often consists of a single turn, achieved by threading the...
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Stability01:28

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The time response of a linear time-invariant (LTI) system can be divided into transient and steady-state responses. The transient response represents the system's initial reaction to a change in input and diminishes to zero over time. In contrast, the steady-state response is the behavior that persists after the transient effects have faded.
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Equivalent Circuits for Practical Transformers01:28

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The practical equivalent circuits of single-phase two-winding transformers exhibit significant deviations from their idealized versions due to the inherent properties of winding resistance and finite core permeability. These properties result in real and reactive power losses, affecting the transformer's performance. Understanding these deviations is crucial for designing more efficient transformers.
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Defect Identification Method for Transformer End Pad Falling Based on Acoustic Stability Feature Analysis.

Shuai Han1, Bowen Wang2, Sizhuo Liao1

  • 1China Electric Power Research Institute, Beijing 100192, China.

Sensors (Basel, Switzerland)
|March 30, 2023
PubMed
Summary
This summary is machine-generated.

This study identifies transformer end pad defects using acoustic signals. Analyzing vibration and sound features with time-series spectrum entropy provides a reliable warning threshold for early fault detection.

Keywords:
Mel time–frequency spectrumdefect identificationstabilitythe end pad falling defecttime-series spectral entropy

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Area of Science:

  • Electrical Engineering
  • Acoustics
  • Mechanical Engineering

Background:

  • Transformer acoustic signals offer valuable diagnostic information.
  • Acoustic signals can be transient or steady-state, varying with operating conditions.
  • Transformer defects, like end pad issues, generate distinct acoustic signatures.

Purpose of the Study:

  • To analyze the vibration mechanism of transformer end pad defects.
  • To extract acoustic features for defect identification.
  • To establish a stability warning threshold for early fault detection.

Main Methods:

  • A quality-spring-damping model was used to analyze defect vibration modes.
  • Short-time Fourier transform and Mel filter banks processed acoustic signals.
  • Time-series spectrum entropy was applied for stability calculations and defect analysis.

Main Results:

  • The time-series spectrum entropy algorithm was validated against simulated samples.
  • Stability calculations were performed on data from 162 operational transformers.
  • A statistically analyzed stability distribution and a warning threshold were established.

Conclusions:

  • The proposed method effectively identifies transformer end pad defects through acoustic signal analysis.
  • The time-series spectrum entropy stability warning threshold demonstrates practical value in fault diagnosis.
  • This approach enables early detection and prevention of transformer failures.